Mycobacterium tuberculosis (MTB) is the principal pathogen which causes tuberculosis (TB), a disease that remains as one of the most alarming health problems worldwide. An active area for the search of new anti-TB therapies is concerned with the use of computational approaches based on Chemoinformatics and/or Bioinformatics toward the discovery of new and potent anti-TB agents. These approaches consider only small series of structurally related compounds and the studies are generally realized for only one target like a protein. This fact constitutes an important limitation. The present work is an effort to overcome this problem. We introduce here the first chemo-bioinformatic approach by developing a multi-target (mt) QSAR discriminant model, for the in silico design and virtual screening of anti-TB agents against six proteins in MTB. The mt-QSAR model was developed by employing a large and heterogeneous database of compounds and substructural descriptors. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. Some fragments were extracted from the molecules and their contributions to anti-TB activity through inhibition of the six proteins, were calculated. Several fragments were identified as responsible for anti-TB activity and new molecular entities were designed from those fragments with positive contributions, being suggested as possible anti-TB agents.

Adsorption of several ionic and non-ionic species (OH-, O, O-, O2-, H, H+ and H-) on the low-index Miller Au (hkl) surfaces has been investigated by means of density functional theory based methods. The stability order for adsorptions on the three surfaces decreases with the increasing of the coordination number of the outermost gold atoms in each surface, i.e., Au(110)>Au(100)>Au(111), which is in agreement with the experimental evidences. The detailed COOPs analysis of the various adsorption sites for all adsorbates in the surface with the most stable adsorption(s), Au(110), evidenced that adsorption in the gold surfaces may be a function of particle size and charge and substantiates the variability in the order of preferences sites for the adsorption of the different species found in these low-index Miller Au( hkl) surfaces. This variability increases with the increasing of the stability of the adsorptions on the gold surfaces, i.e., the Au(110) presents more variability in the order of preferences sites for the adsorption of different species.

In this work, we present results of a molecular dynamics study of poly(ethylene oxide) (PEO) chains of varying length N covalently end-grafted to a strongly attractive siloxane surface in dry conditions. The investigated coverage densities sigma span a wide interval ranging from sparse to very dense coating of the siloxane plane. Our results show that no phase separated clusters are observed because of the strong affinity of ethylene oxide monomers for the siloxane surface. Upon dehydration, the dry systems become contracted and compacted. The monomer and chain-end density distributions are in qualitative accord with theoretical predictions. The brush height scales linearly with N and sigma. The organization of monomers at lower coverage densities is controlled by the attractive interactions with the siloxane support giving rise to an anisotropically collapsed conformation of PEO layer. The highest coverage densities are responsible for the alignment of chains in perpendicular directions with a nematic liquid-like ordering. The balance between the attraction invoked by the siloxane surface and the lateral repulsion at higher sigma seems to appear at sigma = 2.185 nm(-2).

A series of reactions including water, oxygen, hydrogen and nitric oxide dissociation and carbon monoxide or nitric oxide oxidations catalyzed by metallic surfaces have been investigated by means of periodic density functional calculations with the main aim of establishing the importance of spin polarization when the substrate is nonmagnetic. Numerical differences in the calculated total energies and bond lengths of the breaking/forming bonds corresponding to spin restricted or spin unrestricted formalisms are usually smaller than the inherent error of density functional theory based methods. Nevertheless, it is important to insist on the fact that the spin polarized solution exists and is lower in energy than the one corresponding to the spin restricted formalism, as one would expect, and from a practical point of view, results obtained without taking spin polarization into account lead to the same description of the potential energy surface.

Drug development is a long and time-consuming process, which can take anaverage time of 10 years for the identification of one lead compound to be further testedin the preclinical phases. Quantitative Structure-Activity Relationships (QSAR)-basedtechniques are valuable tools for shortening the time of lead compound identification,but also for focusing and limiting the time-costly synthetic activities andbiological/ADMET evaluations. This review reports an overview of the current researchand potential applications of QSAR modelling tools in rational drug design. The chapteris set out in the same order in which QSAR models are generally built up, starting fromthe setup of the dataset for modelling, assembly of typical molecular descriptors andselection routes, followed on by an outline of the commonly used techniques forestablishing the QSAR models and lastly, by a discussion of the most useful proceduresfor reliability and uncertainty assessment of the models for regulatory purposes.

Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.

Diabetes mellitus, a chronic condition caused by defects in insulin secretion, or action, or both, is a group of metabolic disorders whose complications can contribute significantly to ill health, disability, poor quality of life and premature death. From the three main types of diabetes, Type 2 is by far the most common, accounting for about 90% of cases worldwide. Studies on the role of protein tyrosine phosphatase 1B (PTP1B) have clearly shown that it serves as a key negative regulator of insulin signaling and is involved in the insulin resistance associated with Type 2 diabetes. In the present study, a QSAR modeling work was carried out on a series of 1, 2-naphthoquinone derivatives. The inhibitory activity of such compounds was investigated by two types of QSAR methods: multiple linear regression and non-linear neural networks. This strategy afforded QSAR models with good overall accuracy and predictivity on external data, showing it to be a simple, precise and credible tool to predict and screen 1,2-naphoquinone derivatives with high inhibitory activity.